[{"metadata":{"trusted":true},"cell_type":"code","source":"%%javascript\nIPython.OutputArea.prototype._should_scroll = function(lines) {\n return false;\n}","execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"IPython.OutputArea.prototype._should_scroll = function(lines) {\n return false;\n}\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"source: https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide"},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"import pandas as pd\n\ndf = pd.read_excel('COVID-19-geographic-disbtribution-worldwide.xlsx')\ndf1 = df.sort_values(by=['dateRep'])\ndf1['date'] = df1['dateRep'].dt.strftime('%d/%m/%Y')\ndf1['year_month'] = df1['dateRep'].dt.strftime('%Y-%m')\ndf1.drop(df1[(df1['cases'] < 0) | (df1['deaths'] < 0)].index, inplace=True)\ndf1.dropna(subset=['countryterritoryCode'], inplace=True)\ndf1['cumulative_cases'] = df1.groupby(['countryterritoryCode'])['cases'].cumsum()\ndf1['cumulative_deaths'] = df1.groupby(['countryterritoryCode'])['deaths'].cumsum()\ndf1 = df1.rename(columns={\"countryterritoryCode\": \"country_code\", \"countriesAndTerritories\": \"country\", \"popData2019\": \"population2019\", \"continentExp\": \"continent\"})\ndf.head()","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":" dateRep day month year cases deaths countriesAndTerritories geoId \\\n0 2019-12-31 31 12 2019 0 0 Afghanistan AF \n1 2019-12-31 31 12 2019 0 0 Algeria DZ \n2 2019-12-31 31 12 2019 0 0 Armenia AM \n3 2019-12-31 31 12 2019 0 0 Australia AU \n4 2019-12-31 31 12 2019 0 0 Austria AT \n\n countryterritoryCode popData2019 continentExp \\\n0 AFG 38041757.0 Asia \n1 DZA 43053054.0 Africa \n2 ARM 2957728.0 Europe \n3 AUS 25203200.0 Oceania \n4 AUT 8858775.0 Europe \n\n Cumulative_number_for_14_days_of_COVID-19_cases_per_100000 \n0 NaN \n1 NaN \n2 NaN \n3 NaN \n4 NaN ","text/html":" \n\n \n \n dateRep\n day\n month\n year\n cases\n deaths\n countriesAndTerritories\n geoId\n countryterritoryCode\n popData2019\n continentExp\n Cumulative_number_for_14_days_of_COVID-19_cases_per_100000\n \n \n \n \n 0\n 2019-12-31\n 31\n 12\n 2019\n 0\n 0\n Afghanistan\n AF\n AFG\n 38041757.0\n Asia\n NaN\n \n \n 1\n 2019-12-31\n 31\n 12\n 2019\n 0\n 0\n Algeria\n DZ\n DZA\n 43053054.0\n Africa\n NaN\n \n \n 2\n 2019-12-31\n 31\n 12\n 2019\n 0\n 0\n Armenia\n AM\n ARM\n 2957728.0\n Europe\n NaN\n \n \n 3\n 2019-12-31\n 31\n 12\n 2019\n 0\n 0\n Australia\n AU\n AUS\n 25203200.0\n Oceania\n NaN\n \n \n 4\n 2019-12-31\n 31\n 12\n 2019\n 0\n 0\n Austria\n AT\n AUT\n 8858775.0\n Europe\n NaN\n \n \n\n "},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import plotly.express as px\ndf1['size'] = df1['cumulative_cases'].pow(0.2)\nfig = px.scatter_geo(df1, locations=\"country_code\", color=\"cumulative_cases\",\n hover_name=\"country\", size=\"size\",\n animation_frame=\"date\", color_continuous_scale='jet',\n projection=\"equirectangular\", hover_data={'cases':True, 'size':False,'country_code':False},\n title='COVID-19 Spread (31/Dec/2019 - 14/Dec/2020)')\nfig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 2\nfig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 2\nfig.show()","execution_count":30,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"customdata":[[0,0,"AFG"],[0,0,"LBN"],[0,0,"LTU"],[0,0,"LUX"],[0,0,"MYS"],[0,0,"MEX"],[0,0,"MCO"],[0,0,"NPL"],[0,0,"NLD"],[0,0,"NZL"],[0,0,"NGA"],[0,0,"MKD"],[0,0,"NOR"],[0,0,"OMN"],[0,0,"PHL"],[0,0,"KWT"],[0,0,"QAT"],[0,0,"RUS"],[0,0,"SMR"],[0,0,"SGP"],[0,0,"KOR"],[0,0,"ESP"],[0,0,"LKA"],[0,0,"SWE"],[0,0,"CHE"],[0,0,"CNG1925"],[0,0,"THA"],[0,0,"ARE"],[0,0,"GBR"],[0,0,"USA"],[0,0,"VNM"],[0,0,"ROU"],[0,0,"JPN"],[0,0,"PAK"],[0,0,"ISR"],[0,0,"HRV"],[27,1.933182044931763,"CHN"],[0,0,"CAN"],[0,0,"KHM"],[0,0,"BRA"],[0,0,"CZE"],[0,0,"BEL"],[0,0,"BHR"],[0,0,"AZE"],[0,0,"AUT"],[0,0,"AUS"],[0,0,"ARM"],[0,0,"ITA"],[0,0,"BLR"],[0,0,"DNK"],[0,0,"DZA"],[0,0,"IND"],[0,0,"IRL"],[0,0,"IRQ"],[0,0,"IRN"],[0,0,"IDN"],[0,0,"DOM"],[0,0,"ISL"],[0,0,"GRC"],[0,0,"GEO"],[0,0,"FRA"],[0,0,"FIN"],[0,0,"EST"],[0,0,"EGY"],[0,0,"ECU"],[0,0,"DEU"]],"geo":"geo","hovertemplate":"%{hovertext} 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df_dp['continent'].map(colors)","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# from plotly.subplots import make_subplots\n# import plotly.graph_objects as go\n# fig2 = make_subplots(rows=3, cols=10, column_widths=[2, 2, 2, 2, 2, 2, 2, 2, 2, 0.01],\n# specs=[[{\"rowspan\": 2}, None, {\"rowspan\": 2}, None, {\"rowspan\": 2}, None, {\"rowspan\": 2}, None, {}, None],\n# [None, None, None, None, None, None, None, None, {}, None],\n# [None, None, None, None, None, None, None, None, {}, None]],\n# #print_grid=True,\n# subplot_titles=(\"Total Cases\", \"Cases/Population(%)\", \"Total Deaths\", \"Case Fatality(%)\", \"\"))\n\n# fig2.add_trace(go.Bar(x=df_c['cases'], y = df_c['country'], orientation='h', name=\"Total Cases\", hovertext=df_c['continent'], \n# marker_color=df_c['color'], showlegend=False), 1, 1)\n\n# fig2.add_trace(go.Bar(x=df_cp['cases%'], y = df_cp['country'], orientation='h', name=df_cp['continent'], \n# marker_color=df_cp['color'], showlegend=False), 1, 3)\n\n# fig2.add_trace(go.Bar(x=df_d['deaths'], y = df_d['country'], orientation='h', name=df_d['continent'], \n# marker_color=df_d['color'], showlegend=False), 1, 5)\n\n# fig2.add_trace(go.Bar(x=df_dp['deaths%'], y = df_dp['country'], orientation='h', name=df_dp['continent'], \n# marker_color=df_dp['color'], showlegend=False), 1, 7)\n\n# fig2.add_trace(go.Bar(x=[1]*len(colors), y = tuple(colors.keys()), orientation='h', name=t, \n# marker_color=tuple(colors.values()), showlegend=False), 1, 9)\n\n# fig2.update_layout(autosize=False, width=1120, height=1000,title_text=\"Top 20 most Affected Countries\") \n# fig2.show() \n","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nfig = make_subplots(rows=1, cols=7,\n subplot_titles=(\"Total Cases\", \"\", \"Cases/Population(%)\", \"\", \"Total Deaths\", \"\", \"Case Fatality(%)\"))\n\nfig.add_trace(go.Bar(x=df_c['cases'], y=df_c['country'], orientation='h', name=\"Total Cases\", hovertext=df_c['continent']),\n 1, 1)\n\nfig.add_trace(go.Bar(x=df_cp['cases%'], y=df_cp['country'], orientation='h', name=\"Cases/Population(%)\", hovertext=df_cp['continent']),\n 1, 3)\n\nfig.add_trace(go.Bar(x=df_d['deaths'], y=df_d['country'], orientation='h', name=\"Total Deaths\", hovertext=df_d['continent']),\n 1, 5)\n\nfig.add_trace(go.Bar(x=df_dp['deaths%'], y=df_dp['country'], orientation='h', name=\"Case Fatality(%)\", hovertext=df_dp['continent']),\n 1, 7)\n\n#fig.update_yaxes(showticklabels=False)\nfig.update_layout(title_text=\"Top 20 most Affected Countries\", 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Countries, sorted by continent\") \nfig.show() 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